Discovery of Novel GABAAR Allosteric Modulators Through Reinforcement Learning

Abstract

Background: As not all target proteins can be easily screened in vitro, advanced virtual screening is becoming critical.

Objective: In this study, we demonstrate the application of reinforcement learning guided virtual screening for γ-aminobutyric acid A receptor (GABAAR) modulating peptides.

Methods: Structure-based virtual screening was performed on a receptor homology model. Screened molecules deemed to be novel were synthesized and analyzed using patch-clamp analysis.

Results: 13 molecules were synthesized and 11 showed positive allosteric modulation, with two showing 50% activation at the low micromolar range.

Conclusion: Reinforcement learning guided virtual screening is a viable method for the discovery of novel molecules that modulate a difficult to screen transmembrane receptor.

Keywords: Virtual Screening; allosteric; chlorine channel; in silico; peptides; reinforcement learning; structure-based drug design.